Method and apparatus for improving accuracy using edge classification

ABSTRACT

Methods and apparatus of embodiments of the present invention include a classification system configured to treat edge contact of a touch screen as a separate class of touch events such that any touches occurring near the edge of the touch screen are to be processed by a classifier that is configured to process edge contacts as compared to a classifier that is configured to process other contacts that may occur in the approximate middle of the touch screen which may be wholly digitized. An apparatus may employ two separate and distinct classifiers, including a full touch classifier and an edge touch classifier. The touch screen may be configured to have two different sensing regions to determine which of the two classifiers is appropriate for a touch event.

RELATED APPLICATION

This application is a continuation application and claims the priorityof pending U.S. Utility application having Ser. No. 14/492,604 filed onSep. 22, 2014 titled “Method and Apparatus for Improving Accuracy ofTouch Screen Event Analysis by use of Edge Classification.”

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

TECHNICAL FIELD

The present invention relates generally to the field of touch screentechnology and more particularly to the addition of an edge touchclassifier to increase the accuracy of the analysis of touch screenevents.

BACKGROUND

The subject matter discussed in the background section should not beassumed to be prior art merely as a result of its mention in thebackground section. Similarly, a problem mentioned in the backgroundsection or associated with the subject matter of the background sectionshould not be assumed to have been previously recognized in the priorart. The subject matter in the background section merely representsdifferent approaches, which in and of themselves may also be inventions.

Various electronic devices today are typically operated by a userinteracting with a touch sensitive screen. This feature is particularlya characteristic of the recent generation of smart phones. Typically,the touch sensitive screens respond to finger contact to activate thedisplay for further processes. Contact may also be made using tools suchas a stylus or other parts of the hands. The fingers and other contactsmade to the touch sensitive screen generally appear as an activatedpoint or blob (i.e., region). However, when touch contacts occur on theedge of the touch screen, only a portion of the touch contact can bedigitized.

The touch sensitive screen may be associated with a classificationengine which is normally trained on real world touch event data fromusers. However, because edge contacts are rare in ordinary use,classifiers (in the classification engine) receive few edge traininginstances. In response, the classification accuracy of edge touch eventstends to be lower. Further, because less of the touch contact isvisible, there is less data to work with. This leads to severalproblems, most notably that classification algorithms may over-fit tothe limited data. Secondly, edge contacts appear very different tofull-contact, ordinary touches, leading to bi-modal (or evenmulti-modal) distributions of key characteristics to which someclassification algorithms are ill-suited to accommodate. There istherefore a need to mitigate the potential problems associated with edgetouch events that could otherwise reduce the accuracy of suchclassification analysis.

BRIEF SUMMARY

For some embodiments, an apparatus for improving accuracy of touchscreen event analysis comprises two separate and distinct classifiersincluding a full touch classifier and an edge touch classifier. In orderto determine which such classifier is appropriate for each touch event,the touch screen is provided with two similarly distinct sensingregions. One such sensing region is derived from the conventional touchscreen and is generally the bulk of the central area of the touchscreen, but somewhat reduced in size relative to the entire physicalextent of the touch screen. The balance of the touch screen around theperiphery, forms the second sensing region which is everywhere proximateto the screen edge. The precise shape and size of this edge touchsensing region is chosen to enable a high probability that any fingertouch that might extend beyond the edge of the touch screen would betreated as an edge touch event which will instigate edge touchclassifier operation instead of full touch classifier operation duringthe ensuing analysis of the event.

Other aspects and advantages of the present invention can be seen onreview of the drawings, the detailed description and the claims, whichfollow.

BRIEF DESCRIPTION OF THE DRAWINGS

The included drawings are for illustrative purposes and serve only toprovide examples of possible structures and process steps for thedisclosed techniques. These drawings in no way limit any changes in formand detail that may be made to embodiments by one skilled in the artwithout departing from the spirit and scope of the disclosure.

FIG. 1 is a block diagram of a computing system for analyzing a touchscreen event and including two separate and distinct classifiers, onefor full touch events and one for edge touch events;

FIGS. 2A through 2D illustrate four different examples of touch eventson a touch screen;

FIGS. 3A through 3D illustrate what the touch screen “sees” as a resultof the corresponding touch events of FIGS. 2A through 2D;

FIG. 4 is an illustration of an exemplary touch screen having distincttouch screen regions in accordance with one embodiment; and

FIG. 5 is a flow chart drawing of an embodiment of the method of theinvention.

DETAILED DESCRIPTION

Applications of methods and apparatus according to one or moreembodiments are described in this section. These examples are beingprovided solely to add context and aid in the understanding of thepresent disclosure. It will thus be apparent to one skilled in the artthat the techniques described herein may be practiced without some orall of these specific details. In other instances, well known processsteps have not been described in detail in order to avoid unnecessarilyobscuring the present disclosure. Other applications are possible, suchthat the following examples should not be taken as definitive orlimiting either in scope or setting.

In the following detailed description, references are made to theaccompanying drawings, which form a part of the description and in whichare shown, by way of illustration, specific embodiments. Although theseembodiments are described in sufficient detail to enable one skilled inthe art to practice the disclosure, it is understood that these examplesare not limiting, such that other embodiments may be used and changesmay be made without departing from the spirit and scope of thedisclosure.

The aforementioned issues that arise from a user contacting the edges oftouch screens are addressed in embodiments of the present invention byemploying a classification system that treats edge contacts as aseparate and distinct class of touch events. Touches occurring near theedge of the screen are processed by a classifier that is better suitedto process edge contacts (e.g., it has been trained on edge contacts).On the other hand, touches that occur in the middle of the screen (andthus are wholly digitized) are processed by another classifier.

One or more embodiments may be implemented in numerous ways, includingas a process, an apparatus, a system, a device, a method, a computerreadable medium such as a computer readable storage medium containingcomputer readable instructions or computer program code, or as acomputer program product comprising a computer usable medium having acomputer readable program code embodied therein.

The disclosed embodiments may include apparatus for improving accuracyof touch screen event analysis and may comprise two separate anddistinct classifiers. A first classifier may be a full touch classifier.A second classifier may be an edge touch classifier. In order todetermine which of the first or second classifier is appropriate for atouch event, the touch screen is provided with two similarly distinctsensing regions. One such sensing region is derived from theconventional touch screen and is generally the bulk of the central areaof the touch screen, but somewhat reduced in size relative to the entirephysical extent of the touch screen. The balance of the touch screenaround the periphery, forms the second sensing region which iseverywhere proximate to the screen edge. The precise shape and size ofthis edge touch sensing region is chosen to enable a high probabilitythat any finger touch that might extend beyond the edge of the touchscreen would be treated as an edge touch event which will instigate edgetouch classifier operation instead of full touch classifier operationduring the ensuing analysis of the event.

The disclosed embodiments may include a method for improving accuracy oftouch screen event analysis and may comprise detecting a touch event ona touch sensitive screen, said surface having at least two touchregions; generating a vibro-acoustic waveform signal using at least onesensor detecting such touch event; converting the waveform signal intoat least one other form; extracting distinguishing features from saidconverted waveform signal; and classifying said features to analyze thefeatures of the converted touch event waveform signal by employing oneof at least two different classification processes depending on which ofthe two distinct touch regions was touched during the touch event.

The disclosed embodiments may include a machine-readable medium carryingone or more sequences of instructions for providing social information,which instructions, when executed by one or more processors, cause theone or more processors to detect a touch event on a touch sensitivescreen, said surface having at least two touch regions, generate avibro-acoustic waveform signal using at least one sensor detecting suchtouch event, convert the waveform signal into at least one other form,extract distinguishing features from said converted waveform signal, andclassify said features to analyze the features of the converted touchevent waveform signal by employing one of at least two differentclassification processes depending on which of the two distinct touchregions was touched during the touch event.

In general, a user may make contact with a touch sensitive screen of atouch sensitive device or computer system using a stylus or other partsof the hands such as the palm and various parts of the finger, i.e.,pad, nail, knuckle, etc. Each such different type of touch mechanismproduces a different type of digital signature. Moreover, each user of atouch sensitive device may have his or her own unique touch eventcharacteristics resulting from anatomical differences such asfleshiness, finger size, finger shape, BMI and the like. Thesedifferences in touch event characteristics, whether the result ofdifferent user anatomies or different touch mechanisms, may be usedadvantageously to improve the touch screen technology by reducingambiguities, distinguishing between users, responding only tointentional touch events and the like. Such advantageous uses arederived from sophisticated sensor-based analysis of the touch eventcoupled with one or more algorithms designed to provide furtheranalytical characteristics otherwise hidden or not readily apparent inthe data generated by the touch event.

By way of example, one such apparatus is disclosed in pending U.S.patent application Ser. No. 14/483,150 filed on Sep. 11, 2014 by theApplicant hereof and entitled “Method And Apparatus For DifferentiatingTouch Screen Users Based On Touch Event Analysis”. This co-pendingapplication discloses that when a user touches a touch screen amechanical force is applied to the screen resulting in mechanicalvibrations that may be captured by a variety of sensors such as impactsensors, vibration sensors, accelerometers, strain gauges or acousticsensors such as a microphone.

Once the vibro-acoustic signal has been captured, it can be convertedinto a series of features, for example: Average acoustic power, StandardDeviation, Variance, Skewness, Kurtosis, Absolute sum, Root Mean Square(RMS), Dispersion, Zero-crossings, Spectral centroid, Spectral density,Linear Prediction-based Cepstral Coefficients (LPCC), Perceptual LinearPrediction (PLP), Cepstral Coefficients Cepstrum Coefficients,Mel-Frequency Cepstral Coefficients (MFCC), Frequency phases (e.g., asgenerated by an FFT).

Simultaneously, many touch screen technologies are able to digitizeseveral aspects of a touch event, such as the shape, size, capacitance,orientation, pressure, etc. The latter may be used as distinguishingfeatures, or such features can be derived from them.

Because human fingers vary in their anatomical composition, theiracoustic and touch properties can vary between humans. Moreover, the wayusers touch a screen can also be distinguishing (e.g., what finger, whatpart of the finger, how flat, how hard). Thus, the vibro-acousticfeatures and touch features contain properties that can becharacteristic of different users and different parts of user's hands(e.g., fingertip, knuckle, and nail).

It is thus possible to provide a classifier that can run on a touchcomputing device that upon receipt of a touch event, makes a guess aboutwhich user is operating the device, or whether the user is authorized orhas any personalized features. Alternatively, it is also possible toprovide a classifier that can run on a touch computing device that uponreceipt of a touch event, makes a guess about what part of the fingerwas used to contact the screen.

For some embodiments, the disclosed method may include the followingoperations and may employ the following components:

(a) a sensing system that may be configured to continuously samplevibro-acoustic data, saving it into a buffer. This buffer can be of manylengths such as, for example 50 milliseconds;

(b) a touch sensitive screen may be configured wait for a touch event tooccur. Any number of touch technologies may be possible. The operationsof the touch sensitive screen may be configured to operate in parallelwith the sensing system;

(c) when the touch sensitive screen detects a touch event, it may beconfigured to trigger a conversion, feature extraction, andclassification process;

(d) the data from the vibro-acoustic buffer is retrieved. Because thetouch screens typically have some latency, it may be necessary to lookbackwards in the buffer to find the vibro-acoustic waveform thatcorresponds to the touch impact (e.g., if the touch screen has a 20 mslatency, it may be necessary to look back in the buffer 20 ms to findthe corresponding vibro-acoustic event). All or part of the buffer maybe saved and passed to the next operations;

(e) conversion operations may be performed next. The waveform from thesensor is a time-domain representation of the vibro-acoustic signal. Inaddition to saving the waveform, the signal is converted into otherforms. This includes filtering the waveform and transforming into otherforms, including frequency domain representations;

(f) feature extraction operations may be performed next, where touchscreen controller data and vibro-acoustic data are analyzed to extractfeatures that characterize different users. For the vibro-acoustic data,features are computed for all representations of the signal;

(g) these features are then passed to a classification unit, which usesthe information to label the touch event with a user (in addition towhatever the touch sensitive screen reports, e.g., X/Y position,major/minor axes, pressure, etc.);

(h) the augmented touch event is then passed to the OS or end userapplications, to associate a use based on the touch event.

For some embodiments, a classifier may be configured to use one or moreof the following features to perform its operations: location of touchcontact (2D, or 3D in the case of curved glass or other non-planargeometry), size of touch contact (some touch technologies provide anellipse of the touch contact with major and minor axes), rotation of thetouch contact, surface area of the touch contact (e.g., in squared mm orpixels), pressure of touch (available on some touch systems), shear oftouch (“shear stress”, also called “tangential force” in the literature,arises from a force vector perpendicular to the surface normal of atouch screen. This is similar to normal stress—what is commonly calledpressure—which arises from a force vector parallel to the surfacenormal.”), number of touch contacts, capacitance of touch (if using acapacitive touch screen), swept frequency capacitance of touch (if usinga swept frequency capacitive touch screen), and swept frequencyimpedance of touch (if using a swept frequency capacitive touch screen).The computation phase may also compute the derivative of the abovefeatures over a short period of time, for example, touch velocity andpressure velocity. Other features that the classifier may also useinclude shape of touch (some touch technologies can provide the actualshape of the touch, and not just a circle or ellipse), and image of thehand pose (as imaged by e.g., an optical sensor, diffuse illuminatedsurface with camera, near-range capacitive sensing).

The classification engine may use any number of approaches, includingbut not limited to basic heuristics, decision trees, Support VectorMachine, Random Forest, Naïve bayes, elastic matching, dynamic timewarping, template matching, k-means clustering, K-nearest neighborsalgorithm, neural network, Multilayer perceptron, multinomial logisticregression, Gaussian mixture models, and AdaBoost. Additionally, theresults from several different classifiers may be combined through, forexample, a voting scheme.

For some embodiments, it may be possible to use different classifiersbased on one or more features. For example, two classifiers could beemployed, one for processing sensor waveforms with a high StandardDeviation, and another classifier for waveforms with low StandardDeviation.

FIG. 1 is a block diagram of an example computing system for analyzing atouch event based on use of one of two different classifications ofresulting signals in accordance with an embodiment of the presentinvention. The computing system of the embodiment may have an operating(OS), and can run various types of services or applications, known asapps. The computing system may also be equipped with a telecommunicationcapability that can allow connections to a communications network. Sucha computing system may include, but not be limited to, a table topcomputer (e.g., Surface Computing), laptop computer, desktop computer,mobile computer, mobile internet device, mobile phone, smart-phone, PDA(Personal Digital Assistant), game console, portable media player, andthe like.

Referring to FIG. 1, the computing system includes a touch screen 100, atouch event detector 110, a classifier 120 and an OS 130. The touchscreen 100 is an electronic visual display and serves also as aninput/output device supplementing or substituted for a keyboard, amouse, and/or other types of devices. The touch screen 100 displays oneor more interactive elements such as graphical representation forservices or applications designed to perform a specific function on thecomputing system. Touching the interactive elements with the fingerparts of a user, including the conventional tip of the finger, causesthe OS 130 to activate the application or service related to theinteractive elements appropriate to the identified user. Fingers arediverse appendages, both in their motor capabilities and theiranatomical compositions. A single digit contains different parts such asone or more knuckles, a tip, pad and fingernail.

When an object strikes a certain material, vibro-acoustic wavespropagate outward through the material or along the surface of thematerial. Typically, interactive surfaces use rigid materials, such asplastic or glass, which both quickly distribute and faithfully preservethe signal. As such, when one or more fingers touch or contact thesurface of the touch screen 100, vibro-acoustic responses are produced.The vibro-acoustic characteristics of the respective user fingers andtheir respective unique anatomical characteristics produce uniqueresponses for each user.

Referring back to FIG. 1, the touch event detector 110 detects the touchevent entailing the vibro-acoustic signal. The touch event detector 110,for example, may be arranged at a rear side of the touch screen so thatthe vibro-acoustic signal caused by the touch event can be captured. Thetouch event detector 110 can be triggered by the onset of thevibro-acoustic signal resulting from the touch event. To capture thetouch event and subsequent vibro-acoustic signal, the touch eventdetector 110 may include one or more impact sensors, vibration sensors,accelerometers, strain gauges, or acoustic sensors such as a condensermicrophone a piezoelectric microphone, MEMS microphone and the like.Once the vibro-acoustic signal has been captured by the touch eventdetector 110, the vibro-acoustic classifier 120 processes thevibro-acoustic signal to analyze the touch event that activated thetouch screen.

The OS 130 runs the computing system so that the function can beactivated in line with the classification of the vibro-acoustic signalsand the corresponding user. The vibro-acoustic classifier 120 includes asegmentation unit 122 to segment the vibro-acoustic signal into adigital representation; a conversion unit 124 to convert the digitizedvibro-acoustic signal into an electrical signal; a feature extractionunit 126 to derive a series of features from the electrical signal; andtwo classification units 128 and 129 to classify each user using theabove-described features to analyze the touch event depending uponwhether it was a full screen touch event or an edge touch event as willbe further described below.

The segmentation unit 122 samples the vibro-acoustic signal, forexample, at a sampling rate of 96 kHz, using a sliding window of 4096samples of the vibro-acoustic signal. The conversion unit 124 thenperforms, for example, a Fourier Transform on sampled time-dependentvibro-acoustic signal to produce an electrical signal having frequencydomain representation. For example, the Fourier Transform of this windowmay produce 2048 bands of frequency power.

The vibro-acoustic classifier 120 may further down-sample this data intoadditional vectors (i.e., buckets of ten), providing a differentaliasing. In addition, additional time-domain features may be calculatedfrom the vibro-acoustic signal, such as the average absolute amplitude,total absolute amplitude, standard deviation of the absolute amplitude,the center of mass for both the segmented input signal and the FourierTransformed signal, and zero crossings.

The feature extraction unit 126 may also calculate a series of featuresfrom the frequency domain representation of the vibro-acoustic signals,such as the fundamental frequency of the impact waveform. Theclassification units 128 and 129 classify the vibro-acoustic signalusing the features to for example distinguish what user generated thetouch event, so that the computing system may selectively activate afunction related to the identified user depending on the classifiedvibro-acoustic signals.

Referring to FIGS. 2A through 2D, it will be seen that a device 134(i.e., a smart phone) has a touch screen 136. FIGS. 2A and 2B illustratetwo examples of touch events that are fully on screen 136. FIGS. 2C and2D illustrate two further examples of touch events that are partiallyoff the edge of screen 136. FIGS. 3A through 3D illustrate what thetouch screen “sees” as a result of the touch events of FIGS. 2A to 2D.As shown in FIGS. 3A and 3B, fully on screen touch events of FIGS. 2Aand 2B, result in fully configured touch events which, in the embodimentof FIG. 1, are classified by the first classifier 128. However, as shownin FIGS. 3C and 3D, touch events which are partially off the edge of thescreen 136 as depicted in FIGS. 2C and 2D, are “seen” as partiallyconfigured edge touch events which, in the embodiment of FIG. 1, areclassified by the second classifier 129.

One way of determining whether to enable the first classifier 128 orinstead to enable the second classifier 129 for each touch event, isillustrated in FIG. 4. In FIG. 4, a device 134 having a modified touchscreen 135 is illustrated. The modification consists of dividing thetouch screen into two regions 140 and 150. Region 140 is a full touchclassification region and region 150 is an edge touch classificationregion. In the illustrative embodiment if the touch event is entirelywithin full touch region 140, only the first classification unit 128 isenabled. However, if any portion of the touch event is within edge touchregion 150, only the second classification unit 129 is enabled.Alternatively, the centroid (i.e., weighted center) of the touch contactcan be used to decide what classifier is triggered. The full touch firstclassification unit 128 is “trained” to analyze full touch events.However, the edge touch second classification unit 129 is “trained” toanalyze edge touch events. In this manner, classification of touchscreen events has a significant probability of being more accurate thanin those systems that utilize only one classification unit for all touchevents including edge touch events as depicted herein.

To aid classification, the user can provide supplemental trainingsamples to the vibro-acoustic classifier 120. In one exemplaryembodiment, the classification units 128 and 129 may be implemented witha support vector machine (SVM) for feature classification. The SVM is asupervised learning model with associated learning algorithms thatanalyze data and recognize patterns, used for classification andregression analysis.

FIG. 5 illustrates the process steps of an exemplary embodiment of theinvention. As shown therein, in step 140 a touch event is detected on amulti-region touch screen. This results in generation of a waveform instep 142. The waveform is converted in step 144 and features areextracted in step 146. Then classification of the extracted featuresoccurs in either step 148 or step 150 depending upon whether the touchevent occurred in a first or second region.

Thus it will be understood that what has been disclosed herein are anapparatus and method for differentiating between full touch events andedge touch events in a touch sensitive device. At least one sensor maybe employed to detect and capture a waveform signal or acoustical ormechanical effect resulting from a touch event. The waveform signal isconverted, and distinguishing features are extracted for use in twodistinct classification units to associate such features with the user.A full touch event classifier is trained for such full events, while anedge touch event classifier is trained for analyzing only edge touchevents. The appropriate classifier is enabled by a touch screen havingtwo distinct regions, full and edge and in response to the location ofthe touch event relative to these two regions.

These and other aspects of the disclosure may be implemented by varioustypes of hardware, software, firmware, etc. For example, some featuresof the disclosure may be implemented, at least in part, bymachine-readable media that include program instructions, stateinformation, etc., for performing various operations described herein.Examples of program instructions include both machine code, such asproduced by a compiler, and files containing higher-level code that maybe executed by the computer using an interpreter. Examples ofmachine-readable media include, but are not limited to, magnetic mediasuch as hard disks, floppy disks, and magnetic tape; optical media suchas CD-ROM disks; magneto-optical media; and hardware devices that arespecially configured to store and perform program instructions, such asread-only memory (“ROM”) and random access memory (“RAM”).

Any of the above embodiments may be used alone or together with oneanother in any combination. Although various embodiments may have beenmotivated by various deficiencies with the prior art, which may bediscussed or alluded to in one or more places in the specification, theembodiments do not necessarily address any of these deficiencies. Inother words, different embodiments may address different deficienciesthat may be discussed in the specification. Some embodiments may onlypartially address some deficiencies or just one deficiency that may bediscussed in the specification, and some embodiments may not address anyof these deficiencies.

While various embodiments have been described herein, it should beunderstood that they have been presented by way of example only, and notlimitation. Thus, the breadth and scope of the present applicationshould not be limited by any of the embodiments described herein, butshould be defined only in accordance with the following andlater-submitted claims and their equivalents.

What is claimed is:
 1. An apparatus for analyzing touch screen usersbased on characterization of features derived from a touch event; theapparatus comprising: a touch sensitive screen for detecting a touchevent from at least one user, said surface having at least two touchregions; at least one sensor generating a vibro-acoustic waveform signalfrom such touch event; a converter for converting the waveform signalinto a domain signal; a feature extractor for extracting distinguishingfeatures from said domain signal; and a plurality of classificationunits which use the distinguishing features of said extractor to analyzethe features of the domain signal, at least one such classification unitassociated with at least one such touch region and another suchclassification unit associated with another such touch region.
 2. Theapparatus recited in claim 1 wherein said at least one suchclassification unit is associated with an edge region of said touchsensitive screen.
 3. The apparatus recited in claim 1 wherein said atleast one such classification unit is associated with a central regionof said touch sensitive screen.
 4. The apparatus recited in claim 1wherein said touch sensitive screen comprises an edge region and acentral region.
 5. The apparatus recited in claim 1 wherein said touchsensitive screen enables one said classification unit depending uponwhich of said at least two distinct touch regions is touched during atouch event.
 6. The apparatus recited in claim 1 wherein one of saiddistinct touch regions comprises a region proximate to the edge of saidtouch sensitive screen.
 7. The apparatus recited in claim 1 wherein saidtouch sensitive screen comprises a region forming the central area ofsaid touch sensitive screen.
 8. The apparatus recited in claim 1 whereinsaid at least one sensor detects mechanical vibrations initiated by saidtouch event.
 9. The apparatus recited in claim 1 wherein said at leastone sensor comprises a sensor taken from the group of sensors consistingof impact sensors, vibration sensors, accelerometers, strain gauges,piezo-electric devices and acoustic sensors.
 10. The apparatus recitedin claim 1 wherein said distinguishing features are extracted based onat least one computation of a characteristic taken from the groupconsisting of average, standard deviation, variance, skewness, kurtosis,sum, root mean square, crest factor, dispersion, entropy, power sum,center of mass, coefficient of variation, cross-correlation,zero-crossings, seasonality, DC bias, spectral centroid, spectraldensity, spherical harmonics, spectral energy, band energy ratio, byspectral band ratios, cepstral coefficients and Fourier transformcontent.
 11. A method of analyzing touch screen users based oncharacterization of features derived from a touch event; the methodcomprising: detecting a touch event on a touch sensitive screen, saidsurface having at least two touch regions; generating a vibro-acousticwaveform signal using at least one sensor detecting such touch event;converting the waveform signal into a domain signal; extractingdistinguishing features from said domain signal; and classifying saidfeatures to analyze the features of the domain signal by employing oneof at least two different classification processes depending on which ofthe two distinct touch regions was touched during the touch event. 12.The method recited in claim 11 wherein said classifying uses a firstsuch classification process when such touch region that is touched isproximate an edge of said touch sensitive screen.
 13. The method recitedin claim 11 wherein said classifying uses a second such classificationprocess when such touch region that is touched is proximate a centralarea of said touch sensitive screen.
 14. The method recited in claim 11wherein said touch sensitive screen comprises an edge region and acentral region.
 15. The method recited in claim 11 wherein said touchsensitive screen enables one of two different said classificationprocesses depending upon which of said at least two distinct touchregions is touched during a touch event.
 16. The method recited in claim11 wherein one of said distinct touch regions comprises a regionproximate to the edge of said touch sensitive screen.
 17. The methodrecited in claim 11 wherein said one of said distinct touch regionscomprises a region forming the central area of said touch sensitivescreen.
 18. The method recited in claim 11 wherein said at least onesensor detects mechanical vibrations initiated by said touch event. 19.The method recited in claim 11 wherein said at least one sensorcomprises a sensor taken from the group of sensors consisting of impactsensors, vibration sensors, accelerometers, strain gauges,piezo-electric devices and acoustic sensors.
 20. A computer readablemedium containing instructions for using detected touch screen events toanalyze touch screen users where there are at least two touch screenregions, wherein execution of the program instructions by a processorcauses the processor to carry out the steps of: generating avibro-acoustic waveform signal using at least one sensor detecting suchtouch event; converting the waveform signal into a domain signal;extracting distinguishing features from said domain signal; andclassifying said features to analyze the features of the domain signalby employing one of at least two different classification processesdepending on which of the two distinct touch regions was touched duringthe touch event.